Improved super-resolution optical fluctuation imaging by multiple sparse Bayesian learning method

Yuling Li; Ying Liu; Xin Liu

doi:10.1117/12.2500867

23 October 2018 Improved super-resolution optical fluctuation imaging by multiple sparse Bayesian learning method

Yuling Li, Ying Liu, Xin Liu

Proceedings Volume 10820, Optics in Health Care and Biomedical Optics VIII; 1082028 (2018) https://doi.org/10.1117/12.2500867
Event: SPIE/COS Photonics Asia, 2018, Beijing, China

Abstract

By exploiting the statistics of temporal fluorescent fluctuations, super-resolution optical fluctuation imaging (SOFI) can implement a fast super-resolution microscopy imaging, which is suitable for dynamic live-cell imaging. However, the main drawback of SOFI is that the imaging spatial resolution can be surpassed by the localization-based super-resolution microscopy techniques. To address this problem, we propose a new method, which is achieved by using multiple sparse Bayesian learning (M-SBL) method. Since M-SBL method can take into account simultaneously temporal fluctuations and the sparsity priors of emitter, it provides the possibility to obtain an enhancement in spatial resolution compared to standard SOFI (only considering the temporal fluctuations). To measure the performance of our proposed method, we designed three sets of simulation experiments. Firstly, we compared the performance of M-SBL and SOFI in resolving single emitter, and simulation results have demonstrated that the M-SBL method outperforms SOFI. Furthermore, the other simulation data with varying signal to noise and frame number were used to evaluate the performance of M-SBL in resolving fine structures. And the results indicate that when using the proposed M-SBL method, the imaging spatial resolution can be improved compared to the standard SOFI method. Hence, the M-SBL method provides the potential for increasing the temporal resolution of super-resolution microscopy while maintaining a desired spatial resolution.

Citation Download Citation

Yuling Li, Ying Liu, and Xin Liu "Improved super-resolution optical fluctuation imaging by multiple sparse Bayesian learning method", Proc. SPIE 10820, Optics in Health Care and Biomedical Optics VIII, 1082028 (23 October 2018); https://doi.org/10.1117/12.2500867

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